Dynamic Prediction for Multiple Repeated Measures and Event Time Data
Thursday, February 23, 2017
12:30 PM-1:30 PM
The Department of Epidemiology and Biostatistics welcomes Sheng Luo, PhD, Associate Professor in Department of Biostatistics at The University of Texas Health Science Center School of Public Health who will present:
Dynamic Prediction for Multiple Repeated Measures and Event Time Data: an Application to Neurodegenerative Disorders
In many clinical studies of neurodegenerative disorders such as Parkinson's disease (PD) and Amyotrophic lateral sclerosis (ALS), multiple longitudinal outcomes are collected to fully explore the multidimensional impairment caused by these diseases. Moreover, some survival events (e.g., initiation of levodopa therapy in PD and death in ALS) are strongly correlated to the disease status. The personalized dynamic predictions of risks of target events and future health outcome trajectories at every time point, given the subject-specific health outcome profiles, are highly relevant for patient targeting, management, prognosis, and treatment selection. Dr. Luo will propose a joint model that consists of a latent trait linear mixed model (LTLMM) for the multiple longitudinal outcomes, and a survival model for event time. The two submodels are linked together by underlying latent variables. He will develop a fully Bayesian methodology for parameter estimation and dynamic prediction. His proposed model is evaluated by simulation studies and is applied to the clinical studies of PD and ALS.